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In this study, we propose using a neural embedding model-graph neural network(GNN)- that leverages the heterogeneous features of urban areas and theirinteractions captured by human mobility network to obtain vectorrepresentations of these areas. Using large-scale high-resolution mobility datasets from millions of aggregated and anonymized mobile phone users in 16metropolitan counties in the United States, we demonstrate that our embeddingsencode complex relationships among features related to urban components (suchas distribution of facilities) and population attributes and activities. Thespatial gradient in each direction from city center to suburbs is measuredusing clustered representations and the shared characteristics among urbanareas in the same cluster. Furthermore, we show that embeddings generated by amodel trained on a different county can capture 50% to 60% of the emergentspatial structure in another county, allowing us to make cross-countycomparisons in a quantitative way. Our GNN-based framework overcomes thelimitations of previous methods used for examining spatial structures and ishighly scalable. The findings reveal non-linear relationships among urbancomponents and anisotropic spatial gradients in cities. Since the identifiedspatial structures and gradients capture the combined effects of variousmechanisms, such as segregation, disparate facility distribution, and humanmobility, the findings could help identify the limitations of the current citystructure to inform planning decisions and policies. Also, the model andfindings set the stage for a variety of research in urban planning, engineeringand social science through integrated understanding of how the complexinteractions between urban components and population activities and attributesshape the spatial structures in cities.

Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities